Data Governance for Leaders: Turn Data Strategy into Actions

Click to learn more about author Usama Shamma.

Data is everywhere, with IoT and the growing interconnectivity of the different data feeds, data sources, channels, and data consumers, the need for Data Governance controls guided by a Data Strategy are highly arise to support business decisions and fulfill compliance requirements such as European General Data Protection Regulation (GDPR) mandated since May 2018 and BASEL II/III for banking and insurance industry.

There are probably many definitions of Data Governance out there, but any reasonable definition would have to be consistent with the general meaning of governance.

A dictionary definition of govern is “to exercise authority over.

” And authority is “the power or right to give commands, enforce obedience, take action or make final decisions.

” If we are to truly govern our data, we must have systems and processes that ensure the data is consistent with the decisions of the people in authority.

So, “Data Governance” can be defined as the “The formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.

” Because Data Governance is a strategic initiative involving multiple functions across the enterprise, a Data Governance program should include a governing body (steering committee or council), an agreed upon common set of procedures, and a plan to communicate and execute those procedures.

Initially there is a need to analyze the maturity of the current Data Governance (as is state).

This will then lead to developing the organization, policies, procedures and standards, together with the associated Data Governance processes and the necessary supporting technology.

Finally, the human and change management aspects need to be considered, thus there also needs to be training and mentoring provided in development and the deployment.

Data Governance will show the strategy in terms of: Where we are (current situation)What can be done (desired situation)How we are going to get there (Gap Analysis)Implications / Effect on Business and IT departmentImplementation DependenciesRecommended approach for the implementationHow much it will costHow long it will takeWhat resources are needed, staff requirements, and training neededDevelopment schedule and key milestones Data Governance implementation will be resulting in a set of deliverables: Current Data Governance assessment report including pain points Data Governance charter, organization structure and Operating Model.

)Define the different set of processes which need to be utilized across the organization to manage the different pillars of the Data Governance modelDefine Data Governance decision and issue resolution processesDefine SLAs and KPIs related to Data Governance assets.

Define groups, roles and responsibilitiesWork with Data Governance council and board to ensure Data Governance polices and processes are implemented.

Define required dashboards to monitor the progress of development Establish a control mechanism and compliance process to ensure complianceEstablish relationships among Data Governance asset and various assets.

Demonstrate and approve the new Data Governance assets from Data Governance Council and BoardData Governance technology tools application.

The CDO particularly deals with conflicts of interest that may arise among the different data owners and data consumers.

Data Owners; are generally senior managers without the detailed knowledge of data sets and uses, hence they rely on business experts who handle the data.

Data Owners could be accountable but delegate responsibility for detail to Data Stewards.

Their role is to understand what information is held, what is added and what is removed, how information is moved, and who has access and why.

Data Stewards; are normally a Subject Matter Expert (SME) for the dataset they have responsibility for.

Generally, Data Stewards are from the business and understand the true value of the data to the organization.

Data Custodians; are from within the IT function and can make corrections to the data at source.

They have responsibility for the IT infrastructure providing and protecting data in conformance with the policies and practices prescribed by Data Governance.

Data Governance Organization Structure Models Following preferred different models of the Data Governance organization structure: Collaborated teams from IT and lines of businessA separate Data Governance unit that collaborates with the responsible data representatives from the lines of businessEstablish a competency center from the BI team Data Governance Considerations and Success Factors Technology is not the limiting factor for implementing Data GovernancePractical practice of Data Governance focus on data quality monitoring, and control which is a prerequisite for utilizing data and analytics to foster innovation, as well as data integration and SOAAlthough data warehouse has been seen as the only feasible vehicle to establish a harmonized view of enterprise data and is therefore the first natural target for organized governance of data and how data is used, but Data Governance practice targeting the data warehousing have a limited usefulness, and the Data Governance can be fully effective when applied to source systemsData strategy and governance must be closely aligned with the enterprise and digitalization strategy, and with an overarching view of business processesManagement support and the identification of priorities based on corporate strategy are the key Data Governance success factorsTraining and awareness for business users is crucial for Data Governance initiatives success.